Phonemes are the basic unit of any language. If we can separate Bangla phoneme or syllable from speech then performance of speech recognition is increased and space requirement is also less.
We have showed an approach to segment speech into word level. By using relative pitch information, relative energy value syllable segmentation can be done. Segmentation of Bangla speech corpora into sentence level can also be done by using prosodic feature.
Feature of one word may be matched with other word so feature extraction is important. We employed MFCC for feature extraction but delta- delta MFCC coefficient and cepstrum can also be applied for feature extraction.
Neural network can also be used for word separation and their accuracy will be higher than our approach as NN use knowledge. Hidden Markov model can be used as speech recognizer.
A common Bangla database consists of a number of male and female speaker with different age group of different region of Bangladesh is necessary to build to compare recognition performance.
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